Non-parametric object-based approaches to carry out ISA classification from archival aerial orthoimages

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 6, Issue 4, 2013, Pages 2058-2071

  Fernandez Luque, Ismael ^a   Aguilar, Fernando J ^a   Alvarez, M Flor ^b   Aguilar, Manuel Angel ^a  

^a UNIVERSITY OF ALMERÍA   (Spain)

^b UNIVERSITY OF LEÓN   (Spain)

Author keywords

Archival orthoimages; impervious surface area (ISA); nearest neighbor (NN); non parametric classifiers; object based image analysis (OBIA); support vector machines (SVMs); texture features

Indexed keywords

IMPERVIOUS SURFACE AREA; NEAREST NEIGHBORS; NON-PARAMETRIC CLASSIFIERS; OBJECT BASED IMAGE ANALYSIS (OBIA); ORTHOIMAGES; SUPPORT VECTOR MACHINE (SVMS); TEXTURE FEATURES;

IMAGE ANALYSIS; SUPPORT VECTOR MACHINES; TEXTURES;

CLASSIFICATION (OF INFORMATION);

ACCURACY ASSESSMENT; AERIAL PHOTOGRAPHY; ALGORITHM; COASTAL ZONE; IMAGE ANALYSIS; MAP; NEAREST NEIGHBOR ANALYSIS;

EID: 84881043259     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2013.2240265     Document Type: Article

References (81)

1. Q. Weng, "Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends," Remote Sens. Environ., vol. 117, no. 15, pp. 34-39, Feb. 2012.
2. N. Thomas, C. Hendrix, and R. G. Congalton, "A comparison of urban mapping methods using high-resolution digital imagery," Photogramm. Eng. Remote Sens., vol. 69, no. 9, pp. 963-972, Sep. 2003. (Pubitemid 37078316)
3. Q. Weng, "Modeling urban growth effects on surface runoff with the integration of remote sensing and GIS," Environ. Manag., vol. 28, no. 6, pp. 737-748, Dec. 2001. (Pubitemid 33104738)
4. R. R. Gillies, J. B. Box, J. Symanzik, and E. J. Rodemaker, "Effects of urbanization on the aquatic fauna of the Line Creek watershed, Atlanta - A satellite perspective," Remote Sens. Environ., vol. 86, no. 3, pp. 411-422, Aug. 2003. (Pubitemid 37143498)
5. E. T. Slonecker, D. B. Jennings, and D. Garofalo, "Remote sensing of impervious surfaces: A review," Remote Sens. Rev., vol. 20, no. 3, pp. 227-225, Aug. 2001. (Pubitemid 34249066)
6. T. R. Schueler, "The importance of imperviousness," Watershed Protection Techniques, vol. 1, no. 3, pp. 100-111, Mar. 1994.
7. C. L. Arnold Jr. and C. J. Gibbons, "Impervious surface coverage: The emergence of a key environmental indicator," J. Amer. Plan. Assoc., vol. 62, no. 2, pp. 243-258, Mar. 1996.
8. E. Brabec, S. Schulte, and P. L. Richards, "Impervious surfaces and water quality: A review of current literature and its implications for watershed planning," J. Plan. Liter., vol. 16, no. 4, pp. 499-514, May 2002. (Pubitemid 34500267)
9. S. J. Goetz, R. K. Wright, A. J. Smith, E. Zinecker, and E. Schaub, "IKONOS imagery for resource management: Tree cover, impervious surfaces, and Riparian buffer analyses in the mid-Atlantic region," Remote Sens. Environ., vol. 88, no. 1-2, pp. 195-208, Nov. 2003. (Pubitemid 38184696)
10. Y. Zhou and Y. Q.Wang, "Extraction of impervious surface areas from high spatial resolution imagery by multiple agent segmentation and classification," Photogramm. Eng. Remote Sens., vol. 74, no. 7, pp. 857-868, Jul. 2008. (Pubitemid 352013501)
11. D. Lu and Q. Weng, "Extraction of urban impervious surfaces from an IKONOS image," Int. J. Remote Sens., vol. 30, no. 5, pp. 1297-1311, Mar. 2009.
12. J. E.Miller, S. A. C. Nelson, and G.R. Hess, "An object extraction approach for impervious surface classification with very-high-resolution imagery," Prof. Geogr., vol. 61, no. 2, pp. 250-264, May 2009.
13. M. E. Hodgson, J. R. Jensen, J. A. Tullis, K. D. Riordan, and C. M. Archer, "Synergistic use of lidar and color aerial photography for mapping urban parcel imperviousness," Photogramm. Eng. Remote Sens., vol. 69, no. 9, pp. 973-980, Sep. 2003. (Pubitemid 37078317)
14. L. Bruzzone and L. Carlin, "A multilevel context-based system for classification of very high spatial resolution images," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 9, pp. 2587-2600, Sep. 2006. (Pubitemid 44321552)
15. A. S. Laliberte and A. Rango, "Texture and scale in object-based analysis of subdecimeter resolution unmanned aerial vehicle (UAV) imagery," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 3, pp. 1-10, Mar. 2009.
16. S. W. Myint, P. Gober, A. Brazel, S. Grossman-Clarke, and Q. Weng, "Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery," Remote Sens. Environ., vol. 115, no. 5, pp. 1145-1161, May 2011.
17. P. Treitz and P. J. Howarth, "High spatial resolution remote sensing data for forest ecosystem classification: An examination of spatial scale," Remote Sens. Environ., vol. 72, no. 3, pp. 268-289, Jun. 2000. (Pubitemid 30308790)
18. R. Pu, S. Landry, and Q. Yu, "Object-based urban detailed land cover classification with high spatial resolution IKONOS imagery," Int. J. Remote Sens., vol. 32, no. 12, pp. 3285-3308, Jun. 2011.
19. A. P. Carleer and E.Wolff, "Urban land cover multi-level region-based classification of VHR data by selecting relevant features," Int. J. Remote Sens., vol. 27, no. 6, pp. 1035-1051, Mar. 2006. (Pubitemid 43832326)
20. J. Im, J. R. Jensen, and J. A. Tullis, "Object-based change detection using correlation image analysis and image segmentation," Int. J. Remote Sens., vol. 29, no. 2, pp. 399-423, Jan. 2008. (Pubitemid 350269296)
21. D. Lu, S. Hetrick, and E. Moran, "Land Cover classification in a complex urban-rural landscape with QuickBird imagery," Photogramm. Eng. Remote Sens., vol. 76, no. 10, pp. 1159-1168, Oct. 2010.
22. T. Blaschke, "Object based image analysis for remote sensing," ISPRS J. Photogramm. Remote Sens., vol. 65, no. 1, pp. 2-16, Jan. 2010.
23. U. C. Benz, P. Hofmann, G. Willhauck, I. Lingenfelder, and M. Heynen, "Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information," ISPRS J. Photogramm. Remote Sens., vol. 58, no. 3-4, pp. 239-258, Jan. 2004. (Pubitemid 38106043)
24. T. Blaschke and J. Strobl, "What's wrong with pixels? Some recent developments interfacing remote sensing and GIS," GIS - Zeitschrift für Geoinformationssysteme, vol. 14, no. 6, pp. 12-17, Jun. 2001. (Pubitemid 34744385)
25. Y. Gao, J. F. Mas, B. H. P. Maathuis, Z. Xiangmin, and P. M. Van Dijk, "Comparison of pixel-based and object-oriented image classification approaches. A case study in a coal fire area, Wuda, Inner Mongolia, China," Int. J. Remote Sens., vol. 27, no. 18, pp. 4039-4055, Sep. 2006. (Pubitemid 44575063)
26. A. Dehvari and R. J. Heck, "Comparison of object-based and pixel based infrared airborne image classification methods using DEM thematic layer," J. Geogr. Reg. Plan., vol. 2, no. 4, pp. 86-96, Apr. 2009.
27. C. Cleve, M. Kelly, F. R. Kearns, and M. Moritz, "Classification of the wildland-urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography," Comput., Environ. Urban Syst., vol. 32, no. 32, pp. 317-326, Jul. 2008.
28. T. G. Whiteside, G. S. Boggs, and S. W. Maier, "Comparing objectbased and pixel-based classifications for mapping savannas," Int. J. Appl. Earth Obs. Geoinf., vol. 13, no. 6, pp. 884-893, Dec. 2011.
29. D. Lu and Q. Weng, "A survey of image classification methods and techniques for improving classification performance," Int. J. Remote Sens., vol. 28, no. 5, pp. 823-870, Jan. 2007. (Pubitemid 46476334)
30. G. Mountrakis, J. Im, and C. Ogole, "Support vector machines in remote sensing: A review," ISPRS J. Photogramm. Remote Sens., vol. 66, no. 3, pp. 247-259, May 2011.
31. M. Pal and P. M. Mather, "An assessment of the effectiveness of decision treemethods for land cover classification," Remote Sens. Environ., vol. 86, no. 4, pp. 554-565, Aug. 2003. (Pubitemid 37143510)
32. L. Samaniego, A. Bárdossy, and K. Schulz, "Supervised classification of remotely sensed imagery using a modified k-NN technique," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 7, pp. 2112-2125, Jul. 2008.
33. L. Chandelier and G. Martinoty, "A radiometric aerial triangulation for the equalization of digital aerial images and orthoimages," Photogramm. Eng. Remote Sens., vol. 75, no. 2, pp. 193-200, Feb. 2009.
34. C. Song, C. E. Woodcock, K. C. Seto, M. P. Lenney, and S. A. Macomber, "Classification and change detection using Landsat TM data: When and how to correct atmospheric effects?," Remote Sens. Environ., vol. 75, no. 2, pp. 230-244, Feb. 2001. (Pubitemid 32109037)
35. F. Pacifici, F. Del Frate, C. Solimini, andW. J. Emery, "An innovative neural-net method to detect temporal changes in high-resolution optical satellite imagery," IEEE Trans.Geosci. Remote Sens., vol. 45, no. 9, pp. 2940-2952, Sep. 2007. (Pubitemid 47331321)
36. M. Chini, F. Pacifici, W. J. Emery, N. Pierdicca, and F. Del Frate, "Comparing statistical and neural network methods applied to very high resolution satellite images showing changes in man-made structures and rocky flats," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 6, pp. 1812-1821, Jun. 2008. (Pubitemid 351711219)
37. C. Huang, C. Homer, and L. Yang, Methods and Applications for Remote Sensing of Forests: Concepts and Case Studies. Amsterdam, The Netherlands: Kluwer Academic, 2003, ch. 14, pp. 389-410.
38. A. R. Gillespie, A. B. Kahle, and R. E.Walker, "Color enhancement of highly correlated images. II. Channel ratio and "chromaticity" transformation techniques," Remote Sens. Environ., vol. 22, no. 3, pp. 343-365, Aug. 1987.
39. T. Motohka, K. N. Nasahara, H. Oguma, and S. Tsuchida, "Applicability of green-red vegetation index for remote sensing of vegetation phenology," Remote Sens., vol. 2, no. 10, pp. 2369-2387, Oct. 2010.
40. J. A. Gamon and J. S. Surfus, "Assessing leaf pigment content and activity with a reflectometer," New Phytologist, vol. 143, no. 1, pp. 105-117, Jul. 1999. (Pubitemid 29373213)
41. S. Gad and T. Kusky, "Lithological mapping in the eastern desert of Egypt, the Barramiya area, using Landsat thematic mapper (TM)," J. African Earth Sciences, vol. 44, no. 2, pp. 196-202, Feb. 2006. (Pubitemid 43257268)
42. D.Chen, D. A. Stow, and P. Gong, "Examining the effect of spatial resolution and texture window size on classification accuracy: An urban environment case," Int. J. Remote Sens., vol. 25, no. 11, pp. 2177-2192, Jun. 2004. (Pubitemid 38762499)
43. F. Pacifici, M. Chini, and W. J. Emery, "A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification," Remote Sens. Environ., vol. 113, no. 6, pp. 1276-1292, Jun. 2009.
44. F. Agüera, F. J. Aguilar, and M. A. Aguilar, "Using texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses," ISPRS J. Photogramm. Remote Sens., vol. 63, no. 6, pp. 635-646, Nov. 2008.
45. M. Nagao and T. Matsuyama, "Edge preserving smoothing," Comput. Graph. Image Process., vol. 9, no. 4, pp. 394-407, Apr. 1979.
46. R. M. Haralick, K. Shanmugam, and I. Dinstein, "Textural features for image classification," IEEE Trans. Syst., Man Cyber., vol. 3, no. 6, pp. 610-621, Nov. 1973.
47. M. Herold, X. H. Liu, and K. C. Clarke, "Spatial metrics and image texture for mapping urban land use," Photogramm. Eng. Remote Sens., vol. 69, no. 9, pp. 991-1001, Sep. 2003. (Pubitemid 37078319)
48. D. A. Clausi, "An analysis of co-occurrence texture statistics as a function of grey level quantization," Can. J. Remote Sens., vol. 28, no. 1, pp. 45-62, Feb. 2002.
49. Q. Yu, P. Gong, N. Clinton, G. Biging, M. Kelly, and D. Schirokauer, "Object-based detailed vegetation classificationwith airborne high spatial resolution remote sensing imagery," Photogramm. Eng. Remote Sens., vol. 72, no. 7, pp. 799-811, Jul. 2006. (Pubitemid 43993558)
50. A. Baraldi and F. Parmiggiani, "Investigation of the textural characteristics associated with gray level co-occurrencematrix statistical parameters," IEEE Trans. Geosci. Remote Sens., vol. 33, no. 2, pp. 293-304, Mar. 1995.
51. G. Mallinis, N. Koutsias, M. Tsakiri-Strati, and M. Karteris, "Objectbased classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site," ISPRS J. Photogramm. Remote Sens., vol. 63, no. 2, pp. 237-250, Mar. 2008. (Pubitemid 351312872)
52. M. A. Friedl, C. E. Brodley, and A. H. Strahler, "Maximizing land cover classification accuracies produced by decision trees at continental to global scales," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 2, pt. 2, pp. 969-977, Mar. 1999.
53. C. E. Brodley and M. A. Friedl, "Decision tree classification of land cover from remotely sensed data," Remote Sens. Environ., vol. 61, no. 3, pp. 399-409, Sep. 1997. (Pubitemid 28052029)
54. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees. Monterey, CA, USA: Wadsworth and Brooks, 1984.
55. F. Esposito, D. Malerba, and G. Semeraro, "A comparative analysis of methods for pruning decision trees," IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 5, pp. 476-491, Mar. 1997. (Pubitemid 127762239)
56. T. Cover and P. Hart, "Nearest neighbor pattern classification, " IEEE Trans. Inf. Theory, vol. 13, no. 1, pp. 21-27, Jan. 1967.
57. J. M. Keller, M. R. Gray, and J. A. Givens, "Fuzzy k-nearest neighbor algorithm," IEEE Trans. Syst., Man Cybern., vol. SMC-15, no. 4, pp. 580-585, Jul.-Aug. 1985.
58. H. Zhu and O. Basir, "An adaptive fuzzy evidential nearest neighbor formulation for classifying remote sensing images," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 8, pp. 1874-1889, Aug. 2005. (Pubitemid 41329035)
59. Q. Yu, P. Gong, N. Clinton, G. Biging, M. Kelly, and D. Schirokauer, "Object-based detailed vegetation classificationwith airborne high spatial resolution remote sensing imagery," Photogramm. Eng. Remote Sens., vol. 72, no. 7, pp. 799-811, Jul. 2006. (Pubitemid 43993558)
60. R.O. Duda and P. E.Hart, Pattern Classification and Scene Analysis. New York: Wiley, 1973.
61. eCognition Document Version 1.2.1. Munich, Germany, Definiens Imaging, 2010.
62. C. Huang, L. S. Davis, and J. R. G. Townshend, "An assessment of support vector machines for land cover classification," Int. J. Remote Sens., vol. 23, no. 4, pp. 725-749, Feb. 2002. (Pubitemid 34119904)
63. X. Huang and L. Zhang, "Road centreline extraction from high-resolution imagery based on multiscale structural features and support vector machines," Int. J. Remote Sens., vol. 30, no. 8, pp. 1977-1987, Apr. 2009.
64. G. Zhu and D. G. Blumberg, "Classification using ASTER data and SVM algorithms: The case study of Beer Sheva, Israel," Remote Sens. Environ., vol. 80, no. 2, pp. 233-240, May 2002. (Pubitemid 34409346)
65. F. Melgani and L. Bruzzone, "Classification of hyperspectral remote sensing images with support vector machines," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 8, pp. 1778-1790, Aug. 2004.
66. J. Inglada, "Automatic recognition of man-made objects in high resolution optical remote sensing images by SVM classification of geometric image features," ISPRS J. Photogramm. Remote Sens., vol. 62, no. 3, pp. 236-248, Aug. 2007. (Pubitemid 47043389)
67. J. C. Trinder and M. Salah, "Support Vector Machines: Optimization and validation for land cover mapping using aerial images and lidar data," in 34th Int. Symp. Remote Sens., Sydney, Australia, 2011.
68. V. N. Vapnik, The Nature of Statistical Learning Theory. NewYork: Springer-Verlag, 1995.
69. C. Chang and C. Lin, "LIBSVM: A Library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, May 2011.
70. G. M. Foody, A. Mathur, C. Sanchez-Hernandez, and D. S. Boyd, "Training set size requirements for the classification of a specific class," Remote Sens. Environ., vol. 104, no. 1, pp. 1-14, Sep. 2006. (Pubitemid 44262339)
71. G. M. Foody and A.Mathur, "Toward intelligent training of supervised image classifications: Directing training data acquisition for SVMclassification," Remote Sens. Environ., vol. 93, no. 1-2, pp. 107-117, Oct. 2004. (Pubitemid 39234714)
72. X. Zhuang, B. A. Engel,D. F. Lozano-Garcia, R. N. Fernandez, and C. J. Johannsen, "Optimization of training data required for neuro- classification," Int. J. Remote Sens., vol. 15, no. 16, pp. 3271-3277, Aug. 1994.
73. R. G. Congalton, "A review of assessing the accuracy of classifications of remotely sensed data," Remote Sens. Environ., vol. 37, no. 1, pp. 35-46, Jul. 1991.
74. J. R. Jensen, Introductory Digital Image Processing: A Remote Sensing Perspective, 2nd ed. New York: Prentice Hall, 1996.
75. R. G. Congalton and K. Green, Assessing the Accuracy of Remotely Sensed Data, 2th ed. Boca Raton, FL, USA: CRC Press, 2009.
76. J. Sauro and J. R. Lewis, "Estimating completion rates from small samples using binomial confidence intervals: Comparisons and recommendations," in Proc. Human Factors and Ergonomics Soc., Orlando, FL, USA, 2005, pp. 2100-2104. (Pubitemid 351742647)
77. D. Lu, S. Hetrick, and E. Moran, "Impervious surface mapping with QuickBird imagery," Int. J. Remote Sens., vol. 32, no. 9, pp. 2519-2533, May 2011.
78. H. G. Jones and R. A. Vaughan, Remote Sensing of Vegetation: Principles, Techniques, and Applications. Oxford, U.K.: Oxford Univ. Press, 2010.
79. L. Soh and C. Tsatsoulis, "Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 2, pp. 780-795, Mar. 1999.
80. P. Maillard, "Comparing texture analysis methods through classification," Photogramm. Eng. Remote Sens., vol. 69, no. 4, pp. 357-367, Apr. 2003. (Pubitemid 36400420)
81. L. Wang, W. P. Sousa, P. Gong, and G. S. Biging, "Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama," Remote Sens. Environ., vol. 91, no. 3-4, pp. 432-440, Jun. 2004. (Pubitemid 38791557)